An apparatus and associated method for imaging

ABSTRACT

An apparatus configured to generate an output quality error estimate using a machine-learning error estimation model to in compare an output meeting a predetermined quality threshold with an output image reconstructed from a plurality of images, and provide the output quality error estimate for use in estimating if a second subsequent image is required, in addition to a first subsequent image to obtain a cumulative output having an output quality error meeting a predetermined error threshold. Also an apparatus configured, using a received output quality error estimate generated using a machine-learning error estimation model as above, to estimate if a second subsequent image is required, in addition to a first subsequent image, to obtain a cumulative output having an output quality error meeting a predetermined error threshold.

TECHNICAL FIELD

The present disclosure relates to imaging apparatus, associated methodsand computer program code, for example computer tomography (CT) andX-ray imaging. Certain examples relate to apparatus configured to use amachine-learning error estimation model to generate an output qualityerror estimate and/or use an output quality error estimate to determinewhether further images are required to obtain a required reconstructedimage quality.

BACKGROUND

Research is currently being done to improve imaging apparatus andmethods, in particular in relation to radiological imaging such as X-rayand CT imaging.

The listing or discussion of a prior-published document or anybackground in this specification should not necessarily be taken as anacknowledgement that the document or background is part of the state ofthe art or is common general knowledge.

SUMMARY

According to a first aspect, there is provided an apparatus comprisingat least one processor; and at least one memory including computerprogram code, the at least one memory and the computer program codeconfigured to, with the at least one processor, cause the apparatus to:

-   -   generate an output quality error estimate using a        machine-learning error estimation model to compare an output        meeting a predetermined quality threshold with an output image        reconstructed from a plurality of images, and    -   provide the output quality error estimate for use in estimating        if a second subsequent image is required, in addition to a first        subsequent image to obtain a cumulative output having an output        quality error meeting a predetermined error threshold.

According to a further aspect, there is provided an apparatuscomprising: at least one processor; and at least one memory includingcomputer program code, the at least one memory and the computer programcode configured to, with the at least one processor, cause the apparatusto:

-   -   using a received output quality error estimate, generated using        a machine-learning error estimation model by comparing an output        meeting a predetermined quality threshold with an output image        reconstructed from a plurality of images to estimate if a second        subsequent image is required, in addition to a first subsequent        image to obtain a cumulative output having an output quality        error meeting a predetermined error threshold.

The output image used to generate the output quality error estimate maybe reconstructed from a plurality of images recorded using particularimaging parameters, and the first and second subsequent images may berecorded using the particular imaging parameters. In this way the dataused to train the machine-learning error estimation model corresponds tothe subsequent recorded images.

The output quality error estimate may be one or more of:

-   -   a reconstruction error indicating a difference between:        -   an image reconstructed from a plurality of standard power            classed images, the image meeting the predetermined quality            threshold; and        -   an image reconstructed from a plurality of low power classed            images;    -   a diagnostic error indicating a difference between:        -   a diagnosis meeting the predetermined quality threshold; and        -   a diagnosis determined from an image reconstructed from a            plurality of low power classed images; and    -   a segmentation error indicating a difference between:        -   an indication of material type meeting the predetermined            quality threshold obtained from an image reconstructed from            a plurality of standard power classed images; and        -   an indication of material type determined from an image            reconstructed from a plurality of low power classed images.

The predetermined error threshold may be one of:

-   -   an acceptable image noise threshold, and the output quality        error of the cumulative output indicates that image noise of the        cumulative output image meets or is below the predetermined        acceptable image noise threshold;    -   a diagnosis confidence threshold, and the output quality error        of the cumulative output indicates a confidence level that a        diagnosis obtained from the cumulative output exceeds the        predetermined diagnosis confidence threshold; and    -   a segmentation confidence threshold, and the output quality        error of the cumulative output indicates a confidence level that        a segmentation obtained from the cumulative output exceeds the        predetermined segmentation confidence threshold.

The apparatus may be configured to:

-   -   if the output quality error of the cumulative output meets or is        below the predetermined error threshold, provide an indication        to stop recording images; and    -   if the output quality error exceeds the predetermined error        threshold, provide an indication to record the second subsequent        image.

The apparatus may be configured to, if the output quality error of thecumulative output exceeds the predetermined error threshold, obtain anupdated output quality error for the cumulative output including thesecond subsequent image.

The first subsequent image may be taken at a particular angularprojection with respect to the subject, and the second subsequent imagemay be taken at a different particular angular projection with respectto the subject than the first subsequent image. The first and secondsubsequent images may be taken at the same particular angular projectionwith respect to the subject.

The apparatus may be configured to estimate, in a time which is lowenough to allow for the estimating to take place between successivesubsequent images, if the second subsequent image is required. The timeto estimate if a second subsequent image is required may be less than 1second, less than 0.5 seconds, less than 0.2 seconds, or less than 100ms.

The apparatus may be configured to:

-   -   obtain a plurality of subsequent images including the first and        second subsequent images; and    -   after estimating that no further subsequent images are required        to obtain a cumulative output having an output quality error        meeting a predetermined error threshold,    -   obtain the cumulative output by reconstructing an output image        from the plurality of subsequent scans.

The apparatus may be configured to:

-   -   estimate that a cumulative output having an output quality error        meeting a predetermined error threshold cannot be obtained by        recording a second and further subsequent images; and    -   provide an indication to stop recording images.

The first and second subsequent images may be X-ray images, ComputerTomography (CT) scan images, Magnetic Resonance Imaging (MRI) images,functional Magnetic Resonance Imaging (fMRI) images, fusion imaging (acombination of Computer Tomography (CT) imaging and Positron EmissionTomography (PET) imaging), positron emission tomography (PET) images,single photon emission tomography (SPET) images, Magnetoencephalography(MEG) images or ultrasound images.

The apparatus may be configured to estimate if a second subsequent imageis required following one or more of: a single first subsequent image,and a plurality of first subsequent images.

According to a further aspect, there is provided a computer-implementedmethod comprising:

-   -   generating an output quality error estimate using a        machine-learning error estimation model to compare an output        meeting a predetermined quality threshold with an output image        reconstructed from a plurality of images, and    -   providing the output quality error estimate for use in        estimating if a second subsequent image is required, in addition        to a first subsequent image to obtain a cumulative output having        an output quality error meeting a predetermined error threshold.

According to a further aspect, there is provided a computer-implementedmethod comprising:

-   -   using an output quality error estimate, generated using a        machine-learning error estimation model by comparing an output        meeting a predetermined quality threshold with an output image        reconstructed from a plurality of images, to estimate if a        second subsequent image is required, in addition to a first        subsequent image to obtain a cumulative output having an output        quality error meeting a predetermined error threshold.

The steps of any method disclosed herein do not have to be performed inthe exact order disclosed, unless explicitly stated or understood by theskilled person.

Corresponding computer programs for implementing one or more steps ofthe methods disclosed herein are also within the present disclosure andare encompassed by one or more of the described examples.

Thus according to a further aspect, there is provided a computer-programcomprising code configured to:

-   -   generate an output quality error estimate using a        machine-learning error estimation model to compare an output        meeting a predetermined quality threshold with an output image        reconstructed from a plurality of images, and    -   provide the output quality error estimate for use in estimating        if a second subsequent image is required, in addition to a first        subsequent image to obtain a cumulative output having an output        quality error meeting a predetermined error threshold.

Also, according to a further aspect, there is provided acomputer-program comprising code configured to: using an output qualityerror estimate, generated using a machine-learning error estimationmodel by comparing an output meeting a predetermined quality thresholdwith an output image reconstructed from a plurality of images, estimateif a second subsequent image is required, in addition to a firstsubsequent image to obtain a cumulative output having an output qualityerror meeting a predetermined error threshold.

One or more of the computer programs may, when run on a computer, causethe computer to configure any apparatus, including a battery, circuit,controller, or device disclosed herein or perform any method disclosedherein. One or more of the computer programs may be softwareimplementations, and the computer may be considered as any appropriatehardware, including a digital signal processor, a microcontroller, andan implementation in read only memory (ROM), erasable programmable readonly memory (EPROM) or electronically erasable programmable read onlymemory (EEPROM), as non-limiting examples. The software may be anassembly program.

One or more of the computer programs may be provided on a computerreadable medium, which may be a physical computer readable medium suchas a disc or a memory device, may be a non-transient medium, or may beembodied as a transient signal. Such a transient signal may be a networkdownload, including an internet download.

The present disclosure includes one or more corresponding aspects,examples or features in isolation or in various combinations whether ornot specifically stated (including claimed) in that combination or inisolation. Corresponding means for performing one or more of thediscussed functions are also within the present disclosure.

The above summary is intended to be merely exemplary and non-limiting.

BRIEF DESCRIPTION OF THE FIGURES

A description is now given, by way of example only, with reference tothe accompanying drawings, in which:

FIG. 1 shows an example apparatus according to the present disclosure;

FIG. 2 illustrates another example apparatus according to the presentdisclosure;

FIG. 3 illustrates a further example apparatus according to the presentdisclosure;

FIG. 4 illustrates an example scanning process using a machine-learningmodel;

FIG. 5 illustrates an example general training process for amachine-learning model;

FIG. 6 illustrates an example general training process for amachine-learning model to provide an estimated reconstruction error;

FIG. 7 illustrates an example general training process for amachine-learning model to provide an estimated diagnosis error;

FIG. 8 illustrates another example general training process for amachine-learning model to provide an estimated diagnosis error;

FIG. 9 shows the main steps of a method of using the present apparatus;and

FIG. 10 shows the main steps of a method of using the present apparatus;

FIG. 11 shows a computer-readable medium comprising a computer programconfigured to perform, control or enable the methods of FIGS. 9 and/or10.

DESCRIPTION OF SPECIFIC EXAMPLES

In medical X-ray imaging (including CT imaging) it is desirable toreduce/minimize the radiation dose received by the patient. Unnecessarydosage of radiation due to receiving a CT scan or X-ray imaging isharmful for humans and animals. There are growing concerns onradiation-induced genetic, cancerous and other diseases. Also, innon-medical applications, in some cases it may be beneficial to reducethe radiation dose used to investigate, for example, aradiation-sensitive biological or chemical sample of material to obtaininformation about the sample before it breaks down. Certain examplesdescribed herein may provide a technical effect of reducing theradiation dose provided to a subject being scanned.

FIG. 1 shows an apparatus 100 comprising a processor 110, memory 120,input I and output O. In this example only one processor and one memoryare shown but it will be appreciated that other examples may utilisemore than one processor and/or more than one memory (e.g. same ordifferent processor/memory types). The apparatus 100 may be or maycomprise an application specific integrated circuit (ASIC). Theapparatus 100 may be or may comprise a field-programmable gate array(FPGA). The apparatus 100 may be a module for a device, or may be thedevice itself, wherein the processor 110 is a general purpose CPU andthe memory 120 is general purpose memory.

The input I allows for receipt of signalling to the apparatus 100 fromfurther components. The output O allows for onward provision ofsignalling from the apparatus 100 to further components. In this examplethe input I and output O are part of a connection bus that allows forconnection of the apparatus 100 to further components. The processor 110is a general purpose processor dedicated to executing/processinginformation received via the input I in accordance with instructionsstored in the form of computer program code on the memory 120. Theoutput signalling generated by such operations from the processor 110 isprovided onwards to further components via the output O.

The memory 120 (not necessarily a single memory unit) is a computerreadable medium (such as solid state memory, a hard drive, ROM, RAM,Flash or other memory) that stores computer program code. This computerprogram code stores instructions that are executable by the processor110, when the program code is run on the processor 110. The internalconnections between the memory 120 and the processor 110 can beunderstood to provide active coupling between the processor 110 and thememory 120 to allow the processor 110 to access the computer programcode stored on the memory 120.

In this example the input I, output O, processor 110 and memory 120 areelectrically connected internally to allow for communication between therespective components I, O, 110, 120, which may be located proximate toone another as an ASIC. In this way the components I, O, 110, 120 may beintegrated in a single chip/circuit for installation in an electronicdevice. In other examples, one or more or all of the components may belocated separately (for example, throughout a portable electronic devicesuch as devices 200, 300, or within a network such as a “cloud” and/ormay provide/support other functionality).

One or more examples of the apparatus 100 can be used as a component foranother apparatus as in FIG. 2, which shows a variation of apparatus 100incorporating the functionality of apparatus 100 over separatecomponents. In other examples the device 200 may comprise apparatus 100as a module (shown by the optional dashed line box) for a mobile phoneor PDA or audio/video player or the like. Such a module, apparatus ordevice may just comprise a suitably configured memory and processor.

The example apparatus/device 200 comprises a display 240 such as, aLiquid Crystal Display (LCD), e-Ink, or touch-screen user interface(like a tablet PC). The device 200 is configured such that it mayreceive, include, and/or otherwise access data. For example, device 200comprises a communications unit 250 (such as a receiver, transmitter,and/or transceiver), in communication with an antenna 260 for connectionto a wireless network and/or a port (not shown). Device 200 comprises amemory 220 for storing data, which may be received via antenna 260 oruser interface 230. The processor 210 may receive data from the userinterface 230, from the memory 220, or from the communication unit 250.Data may be output to a user of device 200 via the display device 240,and/or any other output devices provided with apparatus. The processor210 may also store the data for later user in the memory 220. The devicecontains components connected via communications bus 280.

The communications unit 250 can be, for example, a receiver,transmitter, and/or transceiver, that is in communication with anantenna 260 for connecting to a wireless network and/or a port (notshown) for accepting a physical connection to a network, such that datamay be received via one or more types of network. The communications (ordata) bus 280 may provide active coupling between the processor 210 andthe memory (or storage medium) 220 to allow the processor 210 to accessthe computer program code stored on the memory 220.

The memory 220 comprises computer program code in the same way as thememory 120 of apparatus 100, but may also comprise other data. Theprocessor 210 may receive data from the user interface 230, from thememory 220, or from the communication unit 250. Regardless of the originof the data, these data may be outputted to a user of device 200 via thedisplay device 240, and/or any other output devices provided withapparatus. The processor 210 may also store the data for later user inthe memory 220.

Device/apparatus 300 shown in FIG. 3 may be an electronic device(including a tablet personal computer), a portable electronic device, aportable telecommunications device, or a module for such a device. Theapparatus 100 can be provided as a module for device 300, or even as aprocessor/memory for the device 300 or a processor/memory for a modulefor such a device 300. The device 300 comprises a processor 385 and astorage medium 390, which are electrically connected by a data bus 380.This data bus 380 can provide an active coupling between the processor385 and the storage medium 390 to allow the processor 380 to access thecomputer program code.

The apparatus 100 in FIG. 3 is electrically connected to an input/outputinterface 370 that receives the output from the apparatus 100 andtransmits this to the device 300 via data bus 380. Interface 370 can beconnected via the data bus 380 to a display 375 (touch-sensitive orotherwise) that provides information from the apparatus 100 to a user.Display 375 can be part of the device 300 or can be separate. The device300 also comprises a processor 385 that is configured for generalcontrol of the apparatus 100 as well as the device 300 by providingsignalling to, and receiving signalling from, other device components tomanage their operation.

The storage medium 390 is configured to store computer code configuredto perform, control or enable the operation of the apparatus 100. Thestorage medium 390 may be configured to store settings for the otherdevice components. The processor 385 may access the storage medium 390to retrieve the component settings in order to manage the operation ofthe other device components. The storage medium 390 may be a temporarystorage medium such as a volatile random access memory. The storagemedium 390 may also be a permanent storage medium such as a hard diskdrive, a flash memory, or a non-volatile random access memory. Thestorage medium 390 could be composed of different combinations of thesame or different memory types.

Examples described herein relate to a machine-learning model which ispre-trained using previously-obtained data, to compare a final resultfrom the previously obtained data (i.e. a complete reconstructed imagefrom several full power scans, or a ground truth diagnosis) with areconstructed scan obtained from previously obtained scans of the sametype as scans to be taken, such as a series of low power scans. In someexamples, the scans to be taken may be recorded using the sameparticular imaging parameters, such as same angular projection, power,and exposure time, as the previously-obtained data. A machine learningmodel can determine a function f such that Y=f(X). In machine-learning,the term “ground truth” refers to data samples, containing well knownand correct pairs of X and Y, which are used to train a model and tovalidate the generalization performance of such a model.

An example of a “low dose” of radiation from a low power scan is betweenapproximately 1-3 mSv (but this may vary depending on the target beingimaged) compared with above approximately 3 mSv for a standard dose. Lowdose X-ray scanning may use multiple low dose scans of the same subject,and an image may be reconstructed from the multiple low-dose scans. Themultiple scans may be recorded from the same position (thus recording aplurality of repeat scans), or may be taken from different positionswith respect to the subject, such as at different angular projections(for example by rotating the subject, or the imaging apparatus, betweenscans).

The machine-learning model therefore “learns” (is provided with datawhich indicates) how a low-power scan from a series of low-power scanscompares with a full “ideal” output (a full 3D reconstructed image or acomplete diagnosis, for example). From this knowledge, themachine-learning model can make a prediction whether a subsequentlow-power scan is likely to provide enough information (along with anyother low-power scans taken in the scanning session) to allow a goodenough reconstruction or diagnosis to be eventually obtained.

Examples described herein include an apparatus configured to generate anoutput quality error estimate by using a machine-learning errorestimation model to compare an output meeting a predetermined qualitythreshold with an output image reconstructed from a plurality of images,and provide the output quality error estimate for use in estimating if asecond subsequent image is required, in addition to a first subsequentimage to obtain a cumulative output having an output quality errormeeting a predetermined error threshold. Such an apparatus may beconsidered to be used in a “training” stage, of training themachine-learning error estimation model for subsequent use.

The machine-learning error estimation model is provided withalready-captured images/information of two types. A first type ofinformation represents an “ideal” or best case, and may be called anoutput meeting a predetermined quality threshold (that is, the output isof a high enough quality that it may be used as required, for example toobtain a diagnosis from, or it is of sufficient resolution thatparticular features can be identified in the image). Examples include afully reconstructed image obtained from a large number of standard powerX-ray scans, or a “ground truth” diagnosis. A second type of informationrepresents the type of data which is going to be obtained in asubsequent imaging/scanning procedure, and may be termed an output imagereconstructed from a plurality of images. Examples include an imagereconstructed from plurality of low power X-ray scans (there may befewer such low power X-ray scans than standard power X-ray scans used toobtain the “ideal” case), and a predicted diagnosis obtained from aplurality of low power scans.

The second type of information may be recorded using the same particularimaging parameters which are also used to capture information insubsequent scans in some examples. The machine-learning error estimationmodel can analyse the subsequently recorded scans, in-between scans, todetermine if those subsequent scans are sufficient to obtain a requiredreconstructed output with the required quality, by determining if thosescans would have an output quality error meeting a predetermined errorthreshold, which indicates a difference between the expectedreconstructed output and an ideal case. The apparatus may determine thata further scan is required to reduce the output quality error and try tomeet the predetermined error threshold. The apparatus may determine thateven if a further scan is obtained, the quality of the reconstructedoutput from the subsequent scans will still not be of a high enoughquality/still not meet the predetermined error threshold.

Examples described herein include an apparatus configured to, using areceived output quality error estimate, generated using amachine-learning error estimation model by comparing an output meeting apredetermined quality threshold with an output image reconstructed froma plurality of images, estimate if a second subsequent image isrequired, in addition to a first subsequent image to obtain a cumulativeoutput having an output quality error meeting a predetermined errorthreshold. Such an apparatus may be considered to be used in a“scanning” or “inference” stage, of subsequently using the trainedmachine-learning error estimation model during scanning a subject.

Examples described herein may allow for estimation of the quality of thereconstructed image online (“on-the-fly”) in a very fast manner, e.g.fast enough to take place between successive scans. This is in contrastto de-noising methods which run offline after all the scans have beenrecorded. De-noising can also be referred to a reconstructing. Deeplearning and convolutional de-noising algorithms (amongst otheriterative reconstruction algorithms) for X-rays and CT scans can be usedto de-noise X-ray images. However, such algorithms are used after allscans have been recorded, and not to decide dynamically (between scans)whether the scanning should continue or not. This is because thesealgorithms can take a long time to run, much longer than can practicallybe spent between taking scans of the same subject. Reconstructing anoutput all scans have been taken requires the scanning power, and thusthe resulting dosage, to be predefined, because a determination of finalreconstructed image quality cannot be obtained during scanning. If thepower is too low, this may result in poor reconstruction quality, andthus the scanning process needs to start again afresh with a higherpower (which may not always be possible depending on determined safeexpose levels).

The fast runtime of examples disclosed herein is achieved by trainingthe machine-learning model offline using a large number of sampleimages, before commencing scanning the present subject. Thus thisoverall method may allow for much lower radiation doses to be requiredthan a maximum determined safe dose, due to the dynamic assessment,between successive scans, of the estimated quality of the finalreconstruction.

As an example, a safe number of scans taken may be determined to be 50before exceeding the recommended radiation exposure due to scanning, butit may be that a good enough image may be reconstructed from only 10such scans. In this example the subsequent 40 scans would not berequired, and by not recording the extra 40 scans, the exposure of thesubject to radiation is reduced compared with recording all 50 scans.

The amount of radiation dose per scan and the speed of successive scansare parameters that can be estimated separately either offline (prior totaking scans) or online (during and/or between taking scans). Theseparameters depend, for example, on the maximum dose, minimum quality,and the speed of the process of determining the output quality error. Insome examples the process of determining the output quality error anddetermining whether or not this meets the predetermined error threshold(which may be performed by a “reconstruction quality estimationalgorithm”, which estimates the quality of a reconstruction which wouldbe obtained from the subsequent scans.

There may be a decision point e.g. after each scan, or less often (e.g.after each group of 3, 5 or 10 scans), at which it is decided whetheranother scan or set of scans should be recorded. Thus, the apparatus maybe configured to estimate if a second subsequent image is requiredfollowing a single first subsequent image (e.g. after each subsequentscan), and/or a plurality of first subsequent images (e.g. after a groupof two or more subsequent scans). For example, the apparatus may makethe estimation as a function of how different the output quality errorof the cumulative output is compared with the output quality errorestimate determined by the machine learning model during training. Forexample, a larger difference in error may cause the apparatus to makethe estimation after a group of a further five subsequent scans arerecorded, whereas a small different in error may cause the apparatus tomake the estimation after each subsequent scan. Other examples arepossible.

The end result of this on-the-fly determination of the requirement forfurther scans may be that fewer images overall are taken compared with anumber of required scans determined offline, or that more lower-powerimages may be taken, thereby reducing the overall dose administeredcompared with a dose determined offline.

Obtaining an indication of whether further scans are required or not(for radiation and non-radiation based scanning) may help to minimise orreduce the time the subject needs to remain stationary during scans. Forexample a claustrophobic person, or child, may be able to stay still inan MRI machine for five minutes but no longer. If it can be determined,by determining if the overall final reconstructed scan will be of goodenough quality after five minutes of scanning, then this avoids thesubject being required to stay still for the otherwise expected time fora series of scans to be taken of e.g. 15 minutes.

Prior to taking scans from the subject (the “subsequent” scans, sincethese scans are recorded subsequent to/following the scans used to trainthe machine learning error estimation model), the machine-learning modelis trained to “learn” about the type of scans which will be taken.

There are several ways of obtaining the data required to train themachine learning model. The machine-learning model may be trained, forexample, using data (e.g. images, diagnoses) already taken from multipleprevious subjects. If the subject to be scanned is, for example, a humanabdomen, then multiple previous scans of human abdomens may be used totrain the machine-learning model. If the subject is suspected of havinga particular medical condition, then multiple previous scans of subjectswith the same particular medical condition may be used to train themachine-learning model.

There also exists a large number of full power scans available fromprevious imaging. The noise that ultra-low-power scanning typicallycreates may be simulated in these scans, and the simulatedultra-low-power scans, together with the corresponding full power scans,may be used to train the machine learning error estimation model, so themodel can be used to estimate the reconstructed scan quality fromsubsequently recorded ultra-low-power scans. Large amounts of bothultra-low-power and full power scans may be collected from phantoms (anobject designed to be imaged which will respond in a similar manner tohow human tissues and organs would act in that specific imagingmodality), cadavers or animals (dead or alive), and this data may beused to train the machine learning model.

Once the data for training purposes has been collected, twomachine-learning models may be built. Firstly, a quality assessmentmodel, may be built, which assesses the quality of the de-noising (thatis, estimates whether a reconstruction from the acquired scans will meetthe predetermined error threshold, indicating that it is of sufficientquality). The quality assessment model is such that at the inferencephase (during the scanning process) it can be run very quickly, so it ispossible to make a dynamic quality assessment of data collected duringthe scanning process (in-between scans). The quality assessment modelmay be called an error estimation model, or a machine learning errorestimation model, because it may be used to estimate an error betweenthe expected reconstructed output from the scan or scans recorded for aparticular subject/scanning procedure, and an “ideal” reconstructionobtained from optimal data e.g. standard power data or a large number ofscans, which meets a predetermined quality threshold, indicating it isgood enough.

Secondly a reconstruction model is required, which creates a de-noisedreconstruction from multiple recorded scans e.g. N consecutive scans ofthe same subject. The final reconstruction model is only run after allthe scans have been taken, so it does not need to be as fast as thequality assessment model. Any suitable reconstruction method can beused. Any machine-learning (e.g. a denoising 2D or 3D convolutionalneural network (CNN)) or inverse modelling method may be used to buildthese models (e.g. analytical, iterative or hybrid CT reconstructionmethods).

It may also be possible to build a combined model. Some approximatereconstruction methods and may be fast enough to be run between scans.Such approximate reconstruction methods may require additionalsupport/information to estimate the quality of a reconstruction.Therefore a combined method that uses a known approximate reconstructionmethod and also uses the machine learning quality estimation describedherein may be used.

Many reconstruction methods exist (e.g. analytical, heuristic andmachine-learning based). These can be used in examples disclosed hereinas part of the quality assessment model, or as part of the finalreconstruction model.

The machine-learning error estimation model has been trained, asdescribed above, to obtain an output quality error estimate. This allowsthe machine-learning error estimation model to be able to correlate ascan recorded following/subsequent to the machine-learning modeltraining with an output quality error which a reconstruction obtainedusing that scan would have. The subsequent scans in some examples may berecorded using the same particular imaging parameters as a correspondingscan used for training the machine-learning model, so that themachine-learning model can use the data is has been trained withregarding a scan of that type and “look up”/indicate an output qualityerror determined for that type of scan. The output quality errorindicates how different the reconstruction would be using thesubsequently recorded scan (and combination of that scan with any othersubsequently recorded scans for the subject in the same imaging session,for example in a series of scans recorded at different angularprojections) from an “ideal” case.

The scanning system may take successive multiple low-power CT scans orX-ray images. During scanning, the pre-trained machine-learning modelcan be used to decide whether to continue the scanning process byrecording further subsequent scans, or terminate it. This decision flowis shown in FIG. 4.

FIG. 4 shows an example process flow for using apparatus as describedherein for scanning a subject. The scans may be, for example, X-rayimages, Computer Tomography scan images, or Magnetic Resonance Imagingimages. One or more of the steps in FIG. 4 may be performed using anapparatus comprising at least one processor; and at least one memoryincluding computer program code.

In this example the machine-learning model has already been trained. Thescanning process starts 404 by defining the maximum dose and the minimumreconstruction quality allowed.

These values may be based on a database of known successful scans andcurrent medical guidelines and legislation of allowed radiation dosage,for example. Also, for some medical conditions, the requiredreconstruction quality may be lower, so a smaller radiation dose will beenough. The minimum reconstruction quality may reflect the outputquality error allowable between a reconstruction of the scans to beobtained and an ideal case (e.g. a reconstruction meeting apredetermined quality threshold).

The next step 406 is to take a low power scan (this can be e.g. one scanin a CT scan round or an ultra-low power 2D X-ray image, for example).The scan is then stored 408 to memory. All the scans taken so far inthis imaging session are available 410 for later reconstruction 420. Ifthe maximum dose is reached 412 following the latest scan 406, then thescanning process stops here. Then the final reconstruction takes place418 using the scans taken 410, and a final reconstructed image 420 isobtained.

If the maximum dose is not yet met 412, then a pre-trained de-noisingquality estimation model (a machine-learning error estimation model) isrun on the scans taken so far 414. This model may be a machine-learningprocess, such as a deep neural network (convolutional neural network(CNN), recurrent neural network (RNN), fully connected (FC) neuralnetwork, or a combination thereof. The output from the qualityestimation model, obtained during the scanning process (i.e. betweenscans) is an output quality error estimate which provides an estimate ofthe quality of the de-noising (that is, an estimate of the quality thata reconstructed output obtained from the scans taken so far would have).The quality estimate can be trained to be specific to the type of thescan or can be a generic de-noising quality estimate.

Steps 414 and 416 together may be considered to use a received outputquality error estimate (a measure of the estimated de-noising orreconstruction quality), generated using a machine-learning errorestimation model by comparing an output meeting a predetermined qualitythreshold with an output image reconstructed from a plurality of images.The generation of the output quality error estimate is discussed in moredetail in relation to FIGS. 5-8. The received output quality errorestimate from the machine-learning model is used to estimate if a secondsubsequent image is required 406, in addition to a first subsequentimage 410 to obtain a cumulative output (e.g. reconstructed image)having an output quality error meeting a predetermined error threshold.That is, the machine-learning model is trained using knownoutputs/output images. Subsequent image scans may then be taken and adetermination made in-between each subsequent scan (or each group of twoor more subsequent scans) whether to continue capturing image scans orwhether to stop capturing image scans (e.g. because the expectedreconstruction from the acquired subsequent scans is of sufficientquality).

Following the estimation of reconstruction quality, if the de-noisingconfidence is good enough 416 (that is, the cumulative output from thesubsequent scans is estimated by the machine learning model to have anoutput quality error meeting a predetermined error threshold), then thefinal reconstruction takes place 418 to obtain a final reconstructedimage 420, and no further scans are taken. If the de-noising confidenceis not good enough 416, then a further scan may be taken 406 providedthe maximum dose has not been reached.

Once the de-noised reconstruction quality has been estimated 414, eitherit is decided to stop the scanning process, or continue it. In otherwords, the apparatus may, if the output quality error of the cumulativeoutput meets or is below the predetermined error threshold (that is, thereconstructed output is expected to have high enough quality), providean indication to stop recording images. If the output quality errorexceeds the predetermined error threshold (that is, the reconstructedoutput is not expected to have high enough quality), the apparatus mayprovide an indication to record the second subsequent image and obtainan updated output quality error for the cumulative output including thesecond subsequent image.

The apparatus may be configured to estimate, in a time which is lowenough to allow for the estimating to take place between successivesubsequent images, if the second subsequent image is required. Becausethe apparatus considers an error in quality rather than a quality perse, the determination of whether a further scan is required or not canbe performed quickly enough to take place between separate subsequentscans of a subject. The time to determine if a second subsequent imageis required may be, for example, less than 1 second, less than 0.5seconds, less than 0.2 seconds, and/or less than 0.1 seconds. It may bethe time is short enough to allow for the estimation to take placebetween recording images of a human or animal subject (thus in a shortenough time that the subject can remain stationary throughout recordingall the subsequent scans).

Once the scanning process has been stopped, a more detailed finalde-noised reconstruction may be created 418 from all of the collectedscans 410. This final reconstruction 420 can be analytical, heuristic,or machine-learning based. Analytical reconstruction methods may bebased on filtered backprojection (FBP), which is based on a onedimensional filter being performed on the projection data beforebackprojecting the data onto the image space. Heuristic methods mayinclude iterative reconstruction methods (IR), which optimize anobjective function iteratively. The objective function may contain adata fidelity term and an edge-preserving term for regularization. Someexamples of IR methods may be slower to run than FBP methods. Machinelearning methods include e.g. the aforementioned convolutional neuralnetwork (CNN) denoising method.

In other words, the apparatus may be configured to obtain a plurality ofsubsequent images including the first and second subsequent images; andafter estimating that no further subsequent images are required toobtain a cumulative output having an output quality error meeting apredetermined error threshold, obtain the cumulative output byreconstructing an output image from the plurality of subsequent scans.In some examples, the apparatus may obtain the cumulative output byreconstructing an output diagnosis (estimate the final diagnosis) fromthe plurality of subsequent scans, and/or obtain the cumulative outputby reconstructing an output segmentation (estimate the finalsegmentation) from the plurality of subsequent scans to indicatematerial types in the imaged subject. In some examples, the apparatusmay obtain the cumulative output by reconstructing an output diagnosis(estimate the final diagnosis) and/or obtain the cumulative output byreconstructing an output segmentation (estimate the final segmentation)using a different method to the one that is used during the scanningprocess. In other words, a diagnosis and/or segmentation may be outputin addition to an image. 2D, 3D, and/or 4D (3D plus the time dimension)outputs may be obtained using examples described herein.

The “reconstruction” may in some examples be a reconstructed imageobtained from separate scan images, so that an estimated image errorbetween an expected reconstructed image from the current data and animage reconstructed from previously obtained image data is obtainedbetween scans, and a complete reconstructed image is not obtainedbetween scans (but may be determined after scanning has finished). Thereconstruction may in some examples be a diagnosis, so that an estimatederror between an expected diagnosis from the current data and adiagnosis from previously obtained data is obtained between scans, and acomplete diagnosis is not obtained between scans (but may be determinedafter scanning has finished). The reconstruction may in some examples bea segmentation (determination of material type regions, e.g. compactbone, spongy bone and bone marrow), so that an estimated error betweenan expected material type from the current data and a material type frompreviously obtained data is obtained between scans, and finaldetermination of material type is not obtained between scans (but may bedetermined after scanning has finished).

In some examples, it may not be possible to obtain a high enough qualityreconstructed output regardless of how many scans are taken (forexample, if there is an error in the scanning equipment, or if the goalof the scanning is to identify a particular object such as a tumour ormass, which is too small to be identified in an image or segmentation).The apparatus may be configured to estimate that a cumulative outputhaving an output quality error meeting a predetermined error thresholdcannot be obtained by recording a second and further subsequent images;and provide an indication to stop recording images. The effect of thismay be to reduce exposure of the subject to radiation, or at leastreduce the extent to which the subject is unnecessarily imaged.

In some examples, the apparatus may comprise one or more of: a centralprocessing unit, a field-programmable gate-array and anapplication-specific integrated circuit. By implementing the apparatus,at least partially, in a dedicated hardware circuit, the estimation maybe performed more quickly than, for example, a software implementationon a general purpose computer/CPU. In some examples, a hardwareaccelerated (FPGA, ASIC) implementation of the quality estimationalgorithm may be used to minimize the latency between the images/scancapture and the decision whether to stop or continue scanning.

FIG. 5 illustrates an example training process to train themachine-learning model prior to recording scans of the subject ofinterest. The machine learning model may be termed a “quality assessmentmodel”, “error estimation model” or “machine learning error estimationmodel” as discussed above. In this example in FIG. 5 a single stochasticgradient descent step of the training is shown in the machine learningprocess. The training can stop when the quality assessment model hasreached a good enough level using validation data (e.g.

known/previously obtained data/scans), or when a certain predefinedamount of training steps/iterations have been taken. This is one exampleof a possible training process, and other training processes may beused.

In this example, a single gradient descent step of the training processuses M low power scans of the same subject 554. M may be chosen so thatthe total dosage corresponds to a typical full power scan. Then thetraining process selects a random subset N (where N<M) of the low powerscans 558 and updates a model 560 between the subset of the low powerscans 558 and the expected reconstruction 556 (which may be obtainedfrom full-power scan taken from the same subject). The model is updated560 by creating a de-noised reconstruction from the subset of N scans.In addition, the quality assessment model may be updated using thedifference between the estimated reconstruction from the M low powerscans, and the target full-power scan, so that the model learns toestimate the expected quality of the reconstruction given the currentscans. A sequential training of the machine-learning system is possible,for instance for a deep neural network, where any variant of stochasticgradient descent can be used.

In some examples, the machine learning model may continue being trainedusing a subsequently acquired scan or scans in conjunction with the dataalready used to trained the model. For example, in transfer learning, apre-trained model can be re-trained with new data to improve performanceor to perform a new task. In multi-task learning, multiple criteria(such as denoising and diagnosis) may be used simultaneously duringtraining. Transfer learning and/or multi-task learning may be used toimprove the training and/or the accuracy of the resulting model.

The trained quality assessment models can be generic (that is, trainedusing a body of data from various subjects, different scan parameters,varying doses, etc.) or can be task specific (that is, trained using abody of known data which corresponds to a subject having e.g. the sameexpected medical condition, or imaging the same body part, etc. as thesubject to be imaged). The model may be generic or specific depending onthe training data used in the training process. For the generic case,the machine learning system may estimate some quantity which can beobtained from the low power scans and compared to the full-power scans,such as mean squared error. In such examples, the output quality errorestimate may be estimated using a machine-learning error estimationmodel to compare an output meeting a predetermined quality thresholdwith an output image reconstructed from a plurality of images, and theoutput quality error estimate may be provided for use in estimating if asecond subsequent image is required, in addition to a first subsequentimage, wherein each subsequent image need not necessarily be recordedusing the same imaging parameters as the plurality of images used toreconstruct the output image used in obtaining the output quality errorestimate, to obtain a cumulative output having an output quality errormeeting a predetermined error threshold.

In task-specific examples, the output quality error estimate may beestimated using a machine-learning error estimation model to compare anoutput meeting a predetermined quality threshold with an output imagereconstructed from a plurality of images recorded using particular (i.e.task-specific) imaging parameters, and the output quality error estimatemay be provided for use in estimating if a second subsequent image isrequired, in addition to a first subsequent image, each subsequent imagerecorded using the same particular imaging parameters, to obtain acumulative output having an output quality error meeting a predeterminederror threshold.

The scans of the subject may also be aligned and unified as part of thescanning process. For instance, in deep learning, higher layers may beinvariant to small changes in the input space, and that invariance maybe used to create an invariant de-noising system. That is, foralignment, there may be many possible method which can be used, but adeep learning system can be able to learn to do the alignment itself.

In some examples, an additional model may be built and used whichdynamically estimates the optimal amount of power required to obtain agood enough reconstructed output, based on previous scans in the currentscanning process and the required quality and total power parameters.

FIG. 6 illustrates an example training process to train themachine-learning model prior to recording scans of the subject ofinterest. FIG. 6 relates to obtaining a reconstructed image of asubject. In FIG. 6, a series of N standard dose scans at different 2Dangular projections 602 are used to reconstruct a 3D reconstruction ofthe imaged subject 608. The 3D reconstruction 608 may be considered tobe an output meeting a predetermined quality threshold. Also, a seriesof M<N ultra-low dose scans at different 2D angular projections 604 areused to reconstruct a 3D reconstruction of the same imaged subject 610.An ultra-low dose may be below approximately 1 mSv, but this may varydepending on the target being imaged. The set of M ultra-low power scansmay in some examples be a subset of the N standard power scans which areartificially altered to model an ultra-low dose image, or may be repeatscans taken at an ultra-low dose rather than at a standard dose, thusthe same angular projection is used in both sets of data 602, 604. Insome examples it need not be the case that the ultra-low dose scans 604are taken at the same angular projection as a corresponding standarddose scan 602.

By comparing the 3D reconstruction from the standard dose scans 608 andthe 3D reconstruction from the ultra-low dose scans 610, areconstruction error 612 may be obtained. This reconstruction errorindicates the difference in quality between a reconstruction obtainedusing standard I (high quality) dose scans and ultra-low (low quality)dose scans. The model only needs to learn to the estimate of thereconstruction error. The actual reconstruction, which can take a longtime (too long to practically be performed in between taking scans ofthe subject) is done after the scans have all been taken.

The machine learning error estimation model 606 is provided with theultra-low dose data 604 as X, and is provided with the correspondingreconstruction error 612 Y for that set of ultra-low dose data.Therefore the machine learning model can determine a function f suchthat Y=f(X), which links the reconstruction error Y 612 to the ultra-lowdose data X 604. In other words, the machine learning model is trainedthat, for a particular ultra-low quality scan 604 X or series of suchscans, the expected difference/error 612 between a 3D reconstruction 610obtained using that ultra-low dose data 604, and an “ideal case” 608, isknown. Thus, once trained, the machine learning model can be used toassess a subsequent ultra-low dose scan, and estimate what the errorwould be between a 3D reconstruction obtained using that subsequent scanor scans, and an ideal case. It can then indicate whether the latestsubsequent scan is enough to obtain a sufficient quality output, andthus scanning may be stopped, or whether a further subsequent scan isrequired to improve/reduce the error to help meet a predetermined errorthreshold.

In this example, the output quality error estimate 612 is areconstruction error indicating a difference between an image 608reconstructed from a plurality of standard power classed images 602, theimage meeting the predetermined quality threshold; and an image 610reconstructed from a plurality of low power classed images 604. In thisexample, the predetermined error threshold to be met by the combinedsubsequent scans is an acceptable image noise threshold, and the outputquality error of the cumulative output indicates that image noise of thecumulative output image would meet or be below the predeterminedacceptable image noise threshold.

In some examples, the first subsequent image may be taken at aparticular angular projection with respect to the subject, and thesecond subsequent image may be taken at a different particular angularprojection with respect to the subject than the first subsequent image.For example, certain CT scans require different angular projections tobe recorded to build up a 3D image of a subject. In other example, thefirst and second subsequent images may be taken at the same particularangular projection with respect to the subject. For example, if a 2Dimage is to be obtained, several low-dose X-ray shots may be taken atthe same angular projection/from the same position, for latercombination to produce a cumulative/combined image.

FIG. 7 illustrates another example training process to train themachine-learning model prior to recording scans of the subject ofinterest. FIG. 7 relates to obtaining data from which a diagnosis of asubject may be obtained. In FIG. 7, a series of scans at different 2Dangular projections 704 are used to reconstruct a 3D reconstruction ofthe same imaged subject 710, and from that 3D reconstruction, adiagnosis may be predicted 714.

The diagnosis 714 predicted from the scan data 704 is compared with a“ground truth” diagnosis 716, and the difference may be termed thediagnosis error 712, which is an output quality error estimate.

Similarly to the machine learning error estimation model of FIG. 6, themachine learning error estimation model 706 is provided with the scandata 704 as X, and is provided with the corresponding diagnosis error712 Y for that set of data. Therefore the machine learning model candetermine a function f such that Y=f(X), which links the diagnosis errorY 712 to the scan data X 704. In other words, the machine learning modelis trained that, for a particular scan or set of scans 704 X, theexpected difference/error 712 in confidence between a diagnosis 710obtained using the scan data 704, and the “ground truth” diagnosis 716,is known. Thus, once trained, the machine learning model can be used toassess a subsequent scan, and estimate what the error in diagnosis wouldbe between a 3D reconstruction obtained using that subsequent scan orscans, and an ideal case. It can then indicate whether the latestsubsequent scan is enough to obtain a sufficient quality diagnosisoutput, and thus scanning may be stopped, or whether a furthersubsequent scan is required to improve/reduce the error to help meet apredetermined error threshold.

In this example, the output quality error estimate 712 is a diagnosiserror indicating a difference between a diagnosis meeting thepredetermined quality threshold 716, and a diagnosis 714 determined froman image 710 reconstructed from a plurality of low power classed images704. In this example, the predetermined error threshold to be met by thecombined subsequent scans is a diagnosis confidence threshold, and theoutput quality error estimate of the cumulative output indicates aconfidence level that a diagnosis obtained from the cumulative outputexceeds the predetermined diagnosis confidence threshold.

In another example (not illustrated), the output quality error estimatemay be a segmentation error indicating a difference between anindication of material type meeting the predetermined quality thresholdobtained from an image reconstructed from a plurality of standard powerclassed images; and an indication of material type determined from animage reconstructed from a plurality of low power classed images. Insuch an example, the predetermined error threshold to be met by thecombined subsequent scans is a segmentation confidence threshold, andthe output quality error of the cumulative output indicates a confidencelevel that a segmentation obtained from the cumulative output exceedsthe predetermined segmentation confidence threshold.

FIG. 8 illustrates another example training process to train themachine-learning model prior to recording scans of the subject ofinterest. FIG. 8 relates to obtaining data from which a diagnosis of asubject may be obtained. In FIG. 8, a series of low dose scans atdifferent 2D angular projections 804 are used to reconstruct a 3Dreconstruction of the same imaged subject 810, and from that 3Dreconstruction 810, a diagnosis may be predicted 814.

The diagnosis 814 predicted from the low dose scan data 804 is comparedwith both a “ground truth” diagnosis 816 (as in FIG. 7), and a predicteddiagnosis 818 obtained from a 3D reconstruction 808 reconstructed from aseries of standard dose scans 802. The difference between the diagnosis814 obtained from the low dose 3D reconstruction 810 and the groundtruth diagnosis 816 and diagnosis 818 obtained from the standard dose 3Dreconstruction 808 may be termed the diagnosis error 812, which is anoutput quality error estimate.

Similarly to the machine learning error estimation models of FIGS. 6 and7, the machine learning error estimation model 806 is provided with thelow dose scan data 804 as X, and is provided with the correspondingdiagnosis error 812 Y for that set of data. Therefore the machinelearning model can determine a function f such that Y=f(X), which linksthe diagnosis error Y 812 to the low dose scan data X 804. In otherwords, the machine learning model is trained that, for a particular scanor set of low dose scans 804 X, the expected difference/error 812 inconfidence between a diagnosis 810 obtained using the low dose scan data804, and the “ground truth” diagnosis 816 or diagnosis 818 obtained froma 3D reconstruction 818 of standard dose scan data 802, is known. Thus,once trained, the machine learning model can be used to assess asubsequent low dose scan, and estimate what the error in diagnosis wouldbe between a 3D reconstruction obtained using that subsequent low dosescan or scans, and an ideal case.

Thus, from FIGS. 6-8, an apparatus may be configured to generate anoutput quality error estimate 612, 712, 812 using a machine-learningerror estimation model 606, 706, 806 to compare an output 608, 716, 816,818 meeting a predetermined quality threshold with an output image 610,710, 810 reconstructed from a plurality of images 604, 704, 804 , andprovide the output quality error estimate 612, 712, 812 for use inestimating if a second subsequent image is required, in addition to afirst subsequent image to obtain a cumulative output having an outputquality error meeting a predetermined error threshold.

Examples disclosed herein may provide for a reduction or minimisation inthe harmful radiation dosage received by the patient due to beingscanned with X-rays, while providing the required quality level of thereconstruction. In some cases the scanning process may be sped up usingexamples disclosed herein due to, for example, identification that nofurther scans are required which may not otherwise be known until allscans in a planned series (e.g. up to a maximum radiation dosage) havebeen taken.

In some examples, algorithms for aligning the different scans may beutilized before the quality assessment and reconstruction algorithms areused. In other words, it may be possible to pre-process the data invarious ways before feeding it in to the machine learning models. Forinstance, when scanning an organ with motion, such as the lungs orheart, there may be existing methods which can be used to align theimages taken at different times. Such alignment pre-processing may beused in combination with quality assessment and reconstructionalgorithms, provided the alignment pre-processing algorithms may be runquickly enough.

FIG. 9 shows the main steps of a method 900 of training a machinelearning error estimation model, namely generating an output qualityerror estimate using a machine-learning error estimation model tocompare an output meeting a predetermined quality threshold with anoutput image reconstructed from a plurality of images 902, and providingthe output quality error estimate for use in estimating if a secondsubsequent image is required, in addition to a first subsequent image toobtain a cumulative output having an output quality error meeting apredetermined error threshold 904.

FIG. 10 shows the method 1000 of using a trained machine learning errorestimation model, namely using an output quality error estimate,generated using a machine-learning error estimation model by comparingan output meeting a predetermined quality threshold with an output imagereconstructed from a plurality of images, to estimate if a secondsubsequent image is required, in addition to a first subsequent image toobtain a cumulative output having an output quality error meeting apredetermined error threshold 1002.

FIG. 11 illustrates schematically a computer/processor readable medium1100 providing a computer program according to one example. The computerprogram may comprise computer code configured to perform, control orenable one or more of the methods of FIGS. 9 and 10. In this example,the computer/processor readable medium 1000 is a disc such as a digitalversatile disc (DVD) or a compact disc (CD). In other examples, thecomputer/processor readable medium 1000 may be any medium that has beenprogrammed in such a way as to carry out an inventive function. Thecomputer/processor readable medium 1000 may be a removable memory devicesuch as a memory stick or memory card (SD, mini SD, micro SD or nano SDcard).

The term “subject” is used to describe the item being imaged, such as anobject, chemical or biological sample, or body (e.g. a human or animalbody or body part/portion, living or dead).

It will be appreciated to the skilled reader that any mentionedapparatus/device and/or other features of particular mentionedapparatus/device may be provided by apparatus arranged such that theybecome configured to carry out the desired operations only when enabled,e.g. switched on, or the like. In such cases, they may not necessarilyhave the appropriate software loaded into the active memory in thenon-enabled (e.g. switched off state) and only load the appropriatesoftware in the enabled (e.g. on state). The apparatus may comprisehardware circuitry and/or firmware. The apparatus may comprise softwareloaded onto memory. Such software/computer programs may be recorded onthe same memory/processor/functional units and/or on one or morememories/processors/functional units.

In some examples, a particular mentioned apparatus/device may bepre-programmed with the appropriate software to carry out desiredoperations, and wherein the appropriate software can be enabled for useby a user downloading a “key”, for example, to unlock/enable thesoftware and its associated functionality. Advantages associated withsuch examples can include a reduced requirement to download data whenfurther functionality is required for a device, and this can be usefulin examples where a device is perceived to have sufficient capacity tostore such pre-programmed software for functionality that may not beenabled by a user.

It will be appreciated that any mentionedapparatus/circuitry/elements/processor may have other functions inaddition to the mentioned functions, and that these functions may beperformed by the same apparatus/circuitry/elements/processor. One ormore disclosed aspects may encompass the electronic distribution ofassociated computer programs and computer programs (which may besource/transport encoded) recorded on an appropriate carrier (e.g.memory, signal).

It will be appreciated that any “computer” described herein can comprisea collection of one or more individual processors/processing elementsthat may or may not be located on the same circuit board, or the sameregion/position of a circuit board or even the same device. In someexamples one or more of any mentioned processors may be distributed overa plurality of devices. The same or different processor/processingelements may perform one or more functions described herein.

It will be appreciated that the term “signalling” may refer to one ormore signals transmitted as a series of transmitted and/or receivedsignals. The series of signals may comprise one, two, three, four oreven more individual signal components or distinct signals to make upsaid signalling. Some or all of these individual signals may betransmitted/received simultaneously, in sequence, and/or such that theytemporally overlap one another.

With reference to any discussion of any mentioned computer and/orprocessor and memory (e.g. including ROM, CD-ROM etc), these maycomprise a computer processor, Application Specific Integrated Circuit(ASIC), field-programmable gate array (FPGA), and/or other hardwarecomponents that have been programmed in such a way to carry out theinventive function.

The applicant hereby discloses in isolation each individual featuredescribed herein and any combination of two or more such features, tothe extent that such features or combinations are capable of beingcarried out based on the present specification as a whole, in the lightof the common general knowledge of a person skilled in the art,irrespective of whether such features or combinations of features solveany problems disclosed herein, and without limitation to the scope ofthe claims. The applicant indicates that the disclosed examples mayconsist of any such individual feature or combination of features. Inview of the foregoing description it will be evident to a person skilledin the art that various modifications may be made within the scope ofthe disclosure.

While there have been shown and described and pointed out fundamentalnovel features as applied to different examples thereof, it will beunderstood that various omissions and substitutions and changes in theform and details of the devices and methods described may be made bythose skilled in the art without departing from the spirit of theinvention. For example, it is expressly intended that all combinationsof those elements and/or method steps which perform substantially thesame function in substantially the same way to achieve the same resultsare within the scope of the invention. Moreover, it should be recognizedthat structures and/or elements and/or method steps shown and/ordescribed in connection with any disclosed form or example may beincorporated in any other disclosed or described or suggested form orexample as a general matter of design choice. Furthermore, in the claimsmeans-plus-function clauses are intended to cover the structuresdescribed herein as performing the recited function and not onlystructural equivalents, but also equivalent structures. Thus although anail and a screw may not be structural equivalents in that a nailemploys a cylindrical surface to secure wooden parts together, whereas ascrew employs a helical surface, in the environment of fastening woodenparts, a nail and a screw may be equivalent structures.

1. An apparatus comprising: at least one processor; and at least onememory including computer program code, the at least one memory and thecomputer program code configured to, with the at least one processor,cause the apparatus to: generate an output quality error estimate usinga machine-learning error estimation model to compare an output meeting apredetermined quality threshold with an output image reconstructed froma plurality of images, and provide the output quality error estimate foruse in estimating if a second subsequent image is required, in additionto a first subsequent image to obtain a cumulative output having anoutput quality error meeting a predetermined error threshold.
 2. Anapparatus comprising: at least one processor; and at least one memoryincluding computer program code, the at least one memory and thecomputer program code configured to, with the at least one processor,cause the apparatus to: use a received output quality error estimate,generated using a machine-learning error estimation model by comparingan output meeting a predetermined quality threshold with an output imagereconstructed from a plurality of images, to estimate if a secondsubsequent image is required, in addition to a first subsequent image toobtain a cumulative output having an output quality error meeting apredetermined error threshold.
 3. The apparatus of claim 1, wherein: theoutput image used to generate the output quality error estimate isreconstructed from a. plurality of images recorded using particularimaging parameters; and the first and second subsequent images arerecorded using the particular imaging parameters.
 4. The apparatus ofclaim 1, wherein the output quality error estimate is one or more of: areconstruction error indicating a difference between: an imagereconstructed from a plurality of standard power classed images, theimage meeting the predetermined quality threshold; and an imagereconstructed from a plurality of low power classed images; a diagnosticerror indicating a difference between: a diagnosis meeting thepredetermined quality threshold; and a diagnosis determined from animage reconstructed from a plurality of low power classed images; or asegmentation error indicating a difference between: an indication ofmaterial type meeting the predetermined quality threshold obtained froman image reconstructed from a plurality of standard power classedimages; and an indication of material type determined from an imagereconstructed from a plurality of low power classed images.
 5. Theapparatus of claim 1, wherein the predetermined error threshold is oneof: an acceptable image noise threshold, and the output quality error ofthe cumulative output indicates that image noise of the cumulativeoutput image meets or is below the predetermined acceptable image noisethreshold; a diagnosis confidence threshold, and the output qualityerror of the cumulative output indicates a confidence level that adiagnosis obtained from the cumulative output exceeds the predetermineddiagnosis confidence threshold; or a segmentation confidence threshold,and the output quality error of the cumulative output indicates aconfidence level that a segmentation obtained from the cumulative outputexceeds the predetermined segmentation confidence threshold.
 6. Theapparatus of claim 1, wherein the apparatus is configured to: if theoutput quality error of the cumulative output meets or is below thepredetermined error threshold, provide an indication to stop recordingimages; and if the output quality error exceeds the predetermined errorthreshold, provide an indication to record the second subsequent image.7. The apparatus of claim 1, wherein the apparatus is further configuredto, if the output quality error of the cumulative output exceeds thepredetermined error threshold, obtain an updated output quality errorfor the cumulative output including the second subsequent image.
 8. Theapparatus of claim 1, wherein the apparatus is configured to estimate,in a time which is low enough to allow for the estimating to take placebetween successive subsequent images, if the second subsequent image isrequired.
 9. The apparatus of claim 1, wherein the apparatus isconfigured to: obtain a plurality of subsequent images including thefirst and second subsequent images; and after estimating that no furthersubsequent images are required to obtain a cumulative output having anoutput quality error meeting a predetermined error threshold, obtain thecumulative output by reconstructing an output image from the pluralityof subsequent scans.
 10. The apparatus of claim 1, wherein the apparatusis configured to: estimate that a cumulative output having an outputquality error meeting a predetermined error threshold cannot be obtainedby recording a second and further subsequent images; and provide anindication to stop recording images.
 11. The apparatus of claim 1,wherein the first and second subsequent images are: X-ray images,Computer Tomography scan images, Magnetic Resonance Imaging images,functional Magnetic Resonance Imaging images, fusion imaging, positronemission tomography images, single photon emission tomography,Magnetoencephalography (MEG) images or ultrasound images.
 12. Theapparatus of claim 1, wherein the apparatus is configured to estimate ifa second subsequent image is required following one or more of: a singlefirst subsequent image, or a plurality of first subsequent images.
 13. Acomputer-implemented method comprising: generating an output qualityerror estimate using a machine-learning error estimation model tocompare an output meeting a predetermined quality threshold with anoutput image reconstructed from a plurality of images, and providing theoutput quality error estimate for use in estimating if a secondsubsequent image is required, in addition to a first subsequent image toobtain a cumulative output having an output quality error meeting apredetermined error threshold.
 14. A computer-implemented methodcomprising: using an output quality error estimate, generated using amachine-learning error estimation model by comparing an output meeting apredetermined quality threshold with an output image reconstructed froma plurality of images, to estimate if a second subsequent image isrequired, in addition to a first subsequent image to obtain a cumulativeoutput having an output quality error meeting a predetermined errorthreshold.
 15. A computer program product comprising a non-transitorycomputer-readable medium having computer readable code stored thereon,the computer readable code, when executed by at least one processor,causing an apparatus to perform the method of claim
 13. 16. A computerprogram product comprising a non-transitory computer-readable mediumhaving computer readable code stored thereon, the computer readablecode, when executed by at least one processor, causing an apparatus toperform the method of claim 14.